Key Specifications
| Vendor | meta |
|---|
| Version | 3.2-3b |
|---|
| Release Date | 2024-09-25 |
|---|
| Context Window | 128000 tokens |
|---|
| Input Modalities | text |
|---|
| Output Modalities | text |
|---|
| License | Llama 3.2 Community License |
|---|
| Documentation | https://llama.meta.com/docs/ |
|---|
Benchmark Performance
| Benchmark | Score | Unit | Evaluated At | Notes | Source |
|---|
| MMLU | 52.5 | % | 2024-09-25 | 5-shot | view |
| HUMANEVAL | 42.5 | pass@1 | 2024-09-25 | — | view |
| GSM8K | 46.4 | % | 2024-09-25 | 0-shot CoT | view |
| MATH | 10 | % | 2024-09-25 | 0-shot CoT | view |
| BBH | 46 | % | 2024-09-25 | 3-shot CoT | view |
| GPQA | 18.8 | % | 2024-09-25 | 0-shot | view |
| IFEVAL | 48.6 | % | 2024-09-25 | prompt_strict | view |
| ARC | 76.3 | % | 2024-09-25 | challenge | view |
| MUSR | 29.3 | % | 2024-09-25 | 0-shot | view |
| WINOGRANDE | 67.8 | % | 2024-09-25 | 0-shot | view |
Pricing
| Tier | Price | Currency |
|---|
| Input | $0.15 / Mtok | USD |
| Output | $0.15 / Mtok | USD |
| Cache Read | $0 / Mtok | USD |
| Cache Write | $0 / Mtok | USD |
Source:
https://ai.meta.com/blog/
· as of 2024-09-25
Compliance
- Data Residency: self-host
- SOC2: ✗
- HIPAA: ✗
- GDPR: ✗
- ISO 27001: ✗
Llama 3.2 3B
Model Overview
Meta Llama 3.2 3B 轻量开源模型, 128K 上下文, 3B 参数, 适合移动设备与边缘部署。
Core Specifications
| Vendor | Version | Release Date | Context Window | Input Modalities | Output Modalities | License |
|---|
| Meta | 3.2-3b | 2024-09-25 | 128K | text | text | Llama 3.2 Community License |
| Benchmark | Score | Unit | Notes |
|---|
| MMLU (Massive Multitask Language Understanding) | 52.5 | % | 5-shot |
| HumanEval | 42.5 | pass@1 | — |
| GSM8K (Grade School Math 8K) | 46.4 | % | 0-shot CoT |
| MATH | 10.0 | % | 0-shot CoT |
| BBH (BIG-Bench Hard) | 46.0 | % | 3-shot CoT |
| GPQA | 18.8 | % | 0-shot |
| IFEval | 48.6 | % | prompt_strict |
| ARC | 76.3 | % | challenge |
| MUSR | 29.3 | % | 0-shot |
| WinoGrande | 67.8 | % | 0-shot |
Pricing
| Input | Output | Cache Read | Cache Write |
|---|
| — | — | — | — |
per million tokens
Strengths
- Reliable general-purpose model.
Weaknesses
- MMLU 52.5, weak knowledge reasoning.
- HumanEval 42.5, coding weak.
- Proprietary, not self-hostable.
Use Cases
References